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SUMMARY:EESS talk on "Machine learning as tool to predict the toxicity of 
 chemicals"
DTSTART:20211214T121500
DTEND:20211214T131500
DTSTAMP:20260407T100410Z
UID:93ebfaa9cdef9eb2f616f8af81402a107586c5e5ef84e1453b77605b
CATEGORIES:Conferences - Seminars
DESCRIPTION:Dr Marco Baity Jesi\, Scientist\, Department Systems Analysis\
 , Integrated Assessment and Modelling\, EAWAG\nAbstract:\nWe apply machine
  learning methods to predict chemical hazard focusing on fish acute toxici
 ty across taxa. We analyze the relevance of taxonomy and experimental setu
 p\, and show taking them into account can lead to large improvements in th
 e classification performance. We quantify the gain obtained by introducing
  the taxonomic and experimental information\, compared to classifying base
 d on chemical information alone. Among the identified relevant features\, 
 the species is the single most important one\, surpassing any single chemi
 cal descriptor. We use our approach with standard machine learning models.
  We are able to obtain accuracies of over 93% on datasets where\, due to n
 oise in the data\, the maximum achievable accuracy is expected to be below
  95%. Most of our models “outperform animal test reproducibility” as m
 easured through recently proposed metrics\, and the best performances are 
 obtained by random forests and deep neural networks. However\, we analyze 
 the metrics that lead to such kinds of statements\, and show that the comp
 arison between machine learning performance and animal test reproducibilit
 y should be addressed with particular care. \n\nShort biography:\nDr Marc
 o Baity-Jesi develops machine-learning algorithms and uses data-oriented m
 ethods to study aquatic systems. After a PhD in theoretical physics in cot
 utorship between Rome and Madrid\, he spent two years as a postdoc in Pari
 s\, working at École Normale\, and two years in New York\, at Columbia Un
 iversity. Since 2019 he is based in Zurich\, as a Group Leader in machine 
 learning at Eawag.
LOCATION:https://epfl.zoom.us/j/63900222242?pwd=OXluejhzTklCbkdWakkvaUFCSG
 Vndz09
STATUS:CONFIRMED
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